Spatio-Temporal Random Voting Scheme For Cognitive Networks

ABSTRACT

A spatio-temporal random voting scheme is provided that incorporates location distribution, spatial randomness, and temporal randomness in the collection of information from a plurality of sensing devices within the cognitive network. The region is divided into a plurality of sectors, where each sector is a portion of the region. A subset of sectors is selected from the plurality of sectors in the region to provide spatial randomness. A device is randomly selected from each sector in the subset of sectors to provide additional spatial randomness to the information collection process. Temporal randomness may be introduced by randomly selecting a timeslot within a sensing window period in which devices are to scan a frequency spectrum band to determine if a signal energy above a threshold is detected. Sensing reports are then collected from the selected sensing devices and used to determine whether the frequency spectrum band is available or in use.

CLAIM OF PRIORITY UNDER 35 U.S.C. §119

The present Application for Patent claims priority to U.S. ProvisionalApplication No. 61/109,091 entitled “Grid-Based Spatio-Temporal RandomVoting”, filed Oct. 28, 2008, assigned to the assignee hereof and herebyexpressly incorporated by reference herein.

BACKGROUND

1. Field

One feature relates to determining spectrum availability in wirelesscommunication systems, and more particularly, to a zone or grid basedvoting method to determine the usage of a frequency spectrum band orchannel based on reports from user sensing devices.

2. Background

In some wireless networks, it may be beneficial for the network todetermine which channels or bands of a frequency spectrum may be in useby others and therefore avoid such channels or bands. For example, incognitive radio networks, primary signal information from sensingdevices is reported to an information center (e.g., Base station). Then,based on the reports, the information center infers whether there existsa primary signal or not. If not, the information center sends a controlsignal to each device to use the spectrum band or channel since it isvacant. Otherwise, the information center and/or sensing devices refrainfrom using the spectrum band.

To disrupt normal service, an attacker can generate a fake primarysignal or mask a primary signal. By faking the primary signal, aninnocent user (device) may never be able to use an otherwise vacantspectrum band. On the other hand, by masking the primary signal, anunknowing device may start using the spectrum band thereby disruptingincumbent user (e.g., devices currently using the spectrum band). Hence,a voting scheme may be utilized to filter out the fake signals ormasking. The simplest voting scheme is to gather all the sensinginformation (N) from each device and infer that there exist a primarysignal if the number of primary signals (m) exceeds certain threshold(i.e., m/N>α).

Two prior art voting schemes include Simple Majority Voting and AbsoluteMajority Voting. The simple majority voting scheme is a form of votingwhere, given two options, the option receiving the most votes wins.Absolute majority voting is another form of voting scheme where thewinner is the option that gets more than half of all possible votesincluding abstentions. In the cognitive radio network context, the tworeport options are: (1) primary signal exist or (2) primary signal doesnot exist.

Both of these prior art voting schemes are not appropriate for cognitiveradio networks as they are susceptible to disruption and security risksby attackers. Consequently, a voting scheme is needed to determinewhether a spectrum band is in use while mitigating the possibility of anattacker.

SUMMARY

A method for collecting information from a plurality of sensing devicesin a cognitive wireless network is provided. A zone-based and/orgrid-based spatio-temporal random voting scheme is used to incorporatelocation distribution, spatial randomness, and temporal randomness inthe collection of information from a plurality of devices within thenetwork.

A region is mapped into a plurality of sectors, where each sector is aportion of the region. In various implementations, the plurality ofsectors may be of approximately equal area or each sector in theplurality of sectors may be selected to encompass approximately the samenumber of sensing devices on average. A subset of sectors may beselected from the plurality of sectors. For instance, the subset ofsectors may be randomly selected from the plurality of sectors so as toachieve spatial randomness. A sensing device from within each sector inthe subset of sectors may be selected from which to collect frequencyspectrum usage information. The sensing device within each selectedsector may be randomly selected so as to achieve intra-sector spatialrandomness. In one example, the same number of sensing devices from eachsector in the subset of sectors may be used to collect information.Selecting a sensing device from within each sector in the subset ofsectors may include: (a) selecting a location within each sector in thesubset of sectors, (b) identifying a first device that is closest tothat location, and/or (c) requesting that first device to scan aparticular spectrum band and indicate whether signal energy above aparticular threshold is detected.

A time slot may be randomly selected within a sensing window period. Thesensing window period may have a fixed time interval, but the start ofthe time slot may be randomly selected for each sensing window period.This time slot may be the period in which the devices are instructed toscan a spectrum band for signal energy above a particular threshold. Amessage may then be sent to the selected sensing devices to collectfrequency spectrum usage information at the selected time slot for aparticular frequency spectrum band, where the collected sensing reportsinclude the frequency spectrum usage information.

Sensing reports may be collected from the selected sensing devices,where each sensing report is indicative of whether the sensing devicedetected a signal energy above a particular threshold in a particularspectrum band. The sensing reports may be collected so as to achievetemporal randomness. A determination can then be made as to whether thedesired spectrum band can be used based on the sensing reports. Forexample, the desired frequency spectrum band can be used if the numberof sensing reports indicating detection of a signal energy above thethreshold is less than half of all collected sensing reports.

Additionally, a method operational in a sensing device is provided forobtaining frequency spectrum usage information for a cognitive wirelessnetwork. A message may be received from the cognitive wireless networkto collect frequency spectrum usage information at a defined time slotfor a particular frequency spectrum band. Frequency spectrum usageinformation is then collected by each sensing device at the indicatedtime slot by scanning the particular spectrum band to determine whethera signal energy above a particular threshold is detected. The sensingdevice may also obtain a geographical position for the sensing device. Asensing report is generated that includes at least one of the frequencyspectrum usage information and the geographical position. The sensingdevice may then send the sensing report to the collection center.Subsequently, the sensing device may receive a transmission from thecognitive wireless network on the frequency spectrum band if thecollection center determines that the frequency spectrum band is unused.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an example of a cognitive networkin which one or more novel features may be implemented.

FIG. 2 is a block diagram illustrating the cognitive network of FIG. 1in which an example of zone-based voting is utilized for informationcollection from a plurality of devices in the network.

FIG. 3 is a block diagram illustrating the cognitive network of FIG. 1in which an example of grid-based voting is utilized for informationcollection from a plurality of devices in the network.

FIG. 4 illustrates an example of temporal random sensing where thesensing time slot occurs randomly within a time interval T and lasts afixed time β.

FIG. 5 is a block diagram illustrating an example of functions that maybe performed by an information collection center for a cognitivewireless network.

FIG. 6 is a diagram illustrating a method operational on an informationcollection center for collecting information from various sensingdevices in a cognitive radio network to implement a voting scheme.

FIG. 7 is a block diagram illustrating an example of a sensing devicethat may operate within a cognitive wireless network to collect and/orreport information related to frequency spectrum usage to a networkentity.

FIG. 8 is a diagram illustrating a method operational in a sensingdevice for providing frequency spectrum usage information to acollection center for a cognitive wireless network.

DETAILED DESCRIPTION

In the following description, specific details are given to provide athorough understanding of the embodiments. However, it will beunderstood by one of ordinary skill in the art that the embodiments maybe practiced without these specific details. For example, circuits maybe shown in block diagrams, or not be shown at all, in order not toobscure the embodiments in unnecessary detail. In other instances,well-known circuits, structures and techniques may not be shown indetail in order not to obscure the embodiments.

Overview

A zone or grid based spatio-temporal random voting scheme is providedthat incorporates location distribution, spatial randomness, andtemporal randomness in the collection of information from a plurality ofsensing devices within the cognitive network. The region is divided intoa plurality of sectors, where each sector is a portion of the region. Asubset of sectors is selected from the plurality of sectors in theregion to provide spatial randomness. A device is randomly selected fromeach sector in the subset of sectors to provide additional spatialrandomness to the information collection process. Moreover, temporalrandomness may be introduced by randomly selecting a timeslot within asensing window period in which devices are to scan a frequency spectrumband to determine if a signal energy above a threshold is detected.Sensing reports are then collected from the selected sensing devices andused to determine whether the frequency spectrum band is available or inuse.

Cognitive Network Environment and Potential Security Risks

FIG. 1 is a block diagram illustrating an example of a cognitive networkin which one or more novel features may be implemented. The cognitivenetwork 100 may include one or more access point(s) 102 that providewireless service to one or more devices 106 and 108 a-g. That is, theaccess point(s) 102 may allow the devices 106 and 108 a-g to communicatewith each other or with other devices on other networks. The devices 106and 108 a-g may include, for example, access terminals, mobile/wirelesscommunication devices, cellular phones, personal digital assistants,mobile computing devices, pagers, among others. The cognitive network100 may be able to perceive current network conditions, plan, decide,act on those conditions, and/or learn from the consequences of itsactions, among other features. Such network conditions may include, forexample, frequency and/or channel usage at any particular time. Forexample, where a frequency space is unallocated (e.g., the frequencyspace or band is available for use by various devices or parties withoutprior arrangement or allocation), the cognitive network 100 wouldideally avoid using a frequency channel or band (within the unallocatedfrequency space) that is being used by other devices (e.g., for purposesother than communicating over the cognitive network). Consequently, thecognitive network 100 may use devices in the network to provide reportsindicating network conditions, including which frequency channels orbands may be in use by others. The cognitive network 100 can then avoidusing a frequency channel or band within the network or in one or moreregions or sectors of the network where a conflicting use is reported.

To collect wireless network conditions, the network 100 may request,collect, and/or receive information from the one or more devices 106 and108 a-g. However, because the cognitive network 100 attempts to learnsuch network conditions from the devices 106 and 108 a-g, it may besusceptible to misinformation and/or attacks.

Such collection of network information may be performed, for example,using a voting scheme. However, there are several security problems whenapplying prior art voting schemes, such as Simple Majority Voting andAbsolute Majority Voting, to a cognitive network.

A first security problem is that if a majority (or a large number) ofsensing devices are located within the same sub-region, the votingscheme can be manipulated by an attacker and may fail. Morespecifically, a large number of devices may be located within ahigh-density region that is part of a larger sensing region. An attackercan use a lower-power fake or masked primary signal within thehigh-density region to fake the devices within that high-density regioninto believing that a primary signal is or is not present (when theopposite is true). If the number of sensing devices in the high-densityregion is greater than the number of devices in the remaining parts ofthe sensing region, this may allow an attacker to succeed in having aspectrum band appear to be in use (when it is not) or appear not to bein use (when it is). For instance, if a threshold value (a) is set tofive (5), an attacker may easily compromise five or more devices withinthe high-density region using a low-power fake primary signal to causethe vote to fail.

A second security problem is that the sensing reports from sensingdevices can cause redundant overhead transmissions if the reports aresent from every device in a wireless network. A solution to address thisissue is sampling the sensing information. However, if the sampling isdone based on a device ID, this can still cause geographically-biased orskewed sampling and an attacker can try to compromise the sampledsensing devices. For example, an attacker may try to obtain closelynumbered device IDs and send fake reports when it is sampled.

A third security problem is that if device sensing is done at regulartime intervals (e.g., every T seconds) during a fixed time slot (e.g.,lasting β seconds); an attacker can manipulate a fake/masked primarysignal only during the fixed time slot to reduce its attack time andpower consumption. Reducing attack time/power has the side-effect ofmaking it harder to trace back an attacker.

Zone-Based Information Collection

FIG. 2 is a block diagram illustrating the cognitive network of FIG. 1in which an example of zone-based voting is utilized for informationcollection from a plurality of devices in the network. In thiszone-based voting scheme, a network region is sub-divided into aplurality of zones or sectors 104 a, 104 b, 104 c, 104 d, 104 e, 104 f,and 104 g. The information may be gathered through one or more accesspoints 102 (e.g., one access point per region or one access pointcovering a plurality of regions). Note that, in one example, each sectormay be defined by location boundaries. Thus, devices that report aposition within the boundaries of a sector are considered to be withinthat sector. An information collection center 110 collects the reportsfrom sensing devices in the network 100, where such reports areindicative of one or more network conditions (e.g., spectrum usage,traffic, noise, interference, etc.).

In one example, the collection center 110 may obtain N reports from eachsector 104 a-g, where N is an integer equal to 1 or greater. Note thatsuch reports may be obtained from sectors that include at least Nsensing devices. For the instance where N=1, the collection center 110may obtain reports from sensing devices 108 a, 108 b, 108 c, 108 d, 108f, and 108 g in sectors 104 a, 104 b, 104 c, 104 e, 104 f and 104 g; nosensing devices are located in sector 104 e. However, the collectioncenter 110 may require that a minimum number of sectors be used in thevoting scheme, so that a single sector is not the only one providing thereport(s). This voting scheme prevents a high concentration of sensingdevices (like devices 106) in one sector 104 c from having adisproportionate effect on the voting results.

According to various implementations, a report may be obtained from asensing device in various ways. For example, the collection center 110may select a sensing device(s) in each sector 104 and send a request tothe selected device(s) in each sector to provide a report. The selecteddevice(s), upon receiving such request, collects and/or sends therequested report to the collection center 110. Alternatively, some orall of the devices in the network may be configured to automatically,regularly, and/or sporadically, collect network information and sendreports to the collection center 110. The collection center 110 mayrandomly, pseudo-randomly, and/or systematically select which reportsfrom each sector to use in the voting scheme.

Grid-Based Information Collection

FIG. 3 is a block diagram illustrating the cognitive network of FIG. 1in which an example of grid-based voting is utilized for informationcollection from a plurality of devices in the network. In thisgrid-based voting scheme, a network region is sub-divided into a grid300 of contiguous cells or sectors 304 of approximately equal area.Network information may be gathered through one or more access points102 (e.g., one access point per region or one access point covering aplurality of regions). Note that, in one example, each sector 304 may bedefined by location boundaries. Thus, devices that report a positionwithin the boundaries of a sector 304 are considered to be within thatsector. The information collection center 110 collects the reports fromsensing devices in the network 100, where such reports are indicative ofone or more network conditions (e.g., spectrum usage, traffic, noise,interference, etc.).

In one example, the collection center 110 may obtain N reports from eachsector 304, where N is an integer equal to one (1) or greater. Note thatsuch reports may be obtained from cells that include at least N sensingdevices. However, the collection center 110 may require that a minimumnumber of sectors be used in the voting scheme, so that a single sectoris not the only one providing the report(s) or the majority of thereports. Additionally, the collection center 110 may restrict the numberof reports used from any particular sector so as thwart attackers thattry to bias the voting by using many devices in a sector to send fakereports. For instance, while sector 304 g may have a high concentrationof sensing devices 106, the collection center 110 just uses a collectionreport from one device 108 c so as not to disproportionately overshadowthe voting or reports from other sectors (e.g., where the collectioncenter is using just one report from other sectors). Consequently, inone example, the collection center 110 utilizes the same number ofreports from each sector (at least for sectors that have at least aminimum number of sensing devices).

Additionally, in some implementations, the collection center 110 mayrandomly select which reports from devices in each sector are to be usedat any one particular time. For example, the collection center 110 mayrandomly select one or more of the devices 106 in the sector 304 g eachtime reports arc collected. Thus, a different device from the pluralityof devices 106 may be used each time reports are collected by thecollection center 110.

The zone and/or grid based information collection methods illustrated inFIGS. 2 and 3 facilitate a voting scheme that thwarts attackers. Forexample, even if devices are densely located or clustered in one sector(e.g., sector 104 c of FIG. 2 or sector 304 g of FIG. 3), only onesensing report from that specific sector is counted in the votingscheme. Another advantage of a zone or grid based information collectionscheme is that even if sensing devices are mobile, network informationfrom those devices can still be collected since they are located withineven distributed regions or sectors.

Note that, in some implementations of the zone and/or grid basedinformation collection methods, the sectors of a network region may bedefined to be approximately the same area. In other implementations, thesectors may be defined to be of different sizes. For instance, thesectors of different sizes (area) may be selected so that they encompassapproximately the same number of sensing devices at any one time or onaverage.

Additionally, another factor that may be considered in selecting thearea of a sector is the expected range of interfering devices. That is,if the unallocated frequency space used by a cognitive wireless networkfor relatively long distance communications is also used by interferingdevices for relatively short range transmission, then the area of thesectors may be defined to approximately coincide with the range of theshort range transmissions. This way, if a particular frequency channelis identified as being in use by other devices or applications in aparticular sector, the collection center (and other components of thecognitive network) may avoid using the identified frequency channel forcommunications at, near, or adjacent to the sector(s) in which thefrequency channel is already being used by other devices orapplications.

Spatial Randomness Information Collection

According to another feature, the cognitive network 100 may implementspatial randomness when collecting network information from devices. Thezone or grid based random voting scheme of FIGS. 2 and 3 providesignificant security advantages over a simple voting scheme. This isbecause information is gathered from geographically distributed regions(e.g., zones, sectors or cells) which allow secure voting even whendevice density variances are present among the regions. That is, bylimiting the effect of regions with a high density of sensing devicesand collecting information from a plurality of regions distributedacross a network area, the effects of any fake reports provided by anattacker can be minimized or eliminated.

Two types or levels of spatial randomness may be implemented, i.e.,region or sector based randomness and/or intra-sector randomness. In thecase of region-based or sector-based randomness, the informationcollection center 102 may decide to use just a subset of the sectors inthe network region to obtain sensing reports. Selecting just a subset ofthe sectors (in the network region) from which to collect informationreduces communication overhead associated with requesting, transmitting,and/or collecting the reports. However, if a fixed subset of sectors isselected, an attacker may try to compromise the fixed subset of sectors.That is, if the fixed subset of grid sectors can be identified by anattacker, the attacker can then attempt to compromise the sensingreports from those sectors. Hence, sectors may be selected in aspatially random fashion. For instance, at a first informationcollection period, the information collection center 102 maypseudo-randomly or randomly select a first plurality of sectors from theavailable sectors in the network region, where the first plurality ofsectors is a first subset of all available sectors in the networkregion. Subsequently, at a second information collection period, theinformation collection center 102 may pseudo-randomly or randomly selecta second plurality of sectors from the available sectors in the networkregion, where the second plurality of sectors is a second subset of allavailable sectors in the network region and the second subset isdifferent than the first subset. Consequently, since the sectors usedeach for each information collection period changes, it prevents anattacker from targeting just a few sectors in a network region.

Additionally, in the case of intra-sector randomness, a sensing devicefrom which a sensing report is requested or obtained may be randomlyselected inside each sector. In one example, for a given informationcollection period, the collection center 110 may determine the sensingdevices present in a sector and randomly or pseudo-randomly selects oneor more devices from which to obtain a report. In another example, thecollection center 110 may randomly select a location (x_(r),y_(r))within a sector and the sensing device that is closest to the selectedlocation (x_(r),y_(r)) is used as the sensing device from which asensing report is to be requested or obtained. In this manner, since thesensing device utilized for each sector is randomly selected for eachinformation collection period, it prevents an attacker from targetingparticular sending devices in the network. This intra-sector spatialrandomness makes it difficult on the attacker to find and compromise aspecific sensing device to achieve their goals.

Note that because fewer than all available sensing devices are used inthe voting scheme, this save significant energy since the reports do nothave to be collected and/or transmitted by every device in the cognitivewireless network.

Temporal Randomness Information Collection

According to yet another feature, temporal randomness may be implementedby the cognitive network. If the information collection period is withina periodic and fixed time slot (e.g., every T seconds during a time slotlasting a fixed time interval β), an attacker may try to disrupt reportcollection by jamming only that fixed time slot, which saves a lot ofpower and time for attacker. This also makes it more difficult to traceback an attacker. Consequently, one feature provides for using temporalrandom when sensing devices collect network information for reports.

FIG. 4 illustrates an example of temporal random sensing where thesensing time slot 402 occurs randomly within a time interval T and lastsa fixed time β. That is, within defined periods of time T, the time slot402 (having a fixed interval β) is randomly selected each time thecollection center 110 seeks to obtain reports from sensing devices sothat an attacker cannot determine when the sensing device is sensinginformation (e.g., ascertaining frequency spectrum usage). During thistime slots 402, the selected sensing devices may scan a frequencyspectrum, band, and/or channel of interest and determine whether asignal above a particular threshold is present in the scanned frequencyspectrum, band, and/or channel. Temporal randomness makes it difficultfor an attacker to find the specific time slot during which a fakesignal or masking signal should to be generated to affect a sensingdevice's information collection.

Example Information Collection Center

FIG. 5 is a block diagram illustrating an example of functions that maybe performed by an information collection center for a cognitivewireless network. The information collection center 502 may include aRegion Mapping Module 504 that maps or divides a network region intomultiple sectors. In one example, this may be accomplished simply byassociating a different (directional) antenna covering a sub-region of anetwork with a particular sector. Alternatively, a region may be dividedinto a plurality of contiguous sectors of approximately equal area. Inyet another alternative implementation, a region may be divided into aplurality of sectors of different areas but with approximately equalsensing devices operating therein.

A Sector Selection Module 506 may then select one or more sectors of thenetwork region in a random or pseudo-random fashion. This providesspatial diversity or randomness in the selection of sectors from whichthe collection center 502 obtains sensing reports.

For each selected sector, a Device Selector Module 508 then selects asensing device located within that selected sector from which to collectinformation. The Device Selector Module 508 provides additional spatialdiversity or randomness to the voting scheme. Such collected informationmay indicate whether the selected sensing device in the selected sectordetects or recognizes a signal above a certain threshold in a particularspectrum band or channel. Additionally a Sensing Time Slot Selector 510may randomly or pseudo-randomly select when such information iscollected. That is, during this time slot, the information collectioncenter 502 may send a message to the selected sensing devices to scan aparticular frequency spectrum band or channel for signals above aparticular threshold. In an alternative implementation, each sensingdevice may scan the frequency spectrum band at random times and providesits latest report to the information collection center 502 whenrequested.

A Sensing Report Collector 512 then collects information received fromthe selected sensing devices. Based on the received sensing reports, aChannel Selector Module 514 may then determine whether the particularfrequency spectrum band or channel can be used for new communications bythe wireless network.

Note that the various modules of the information collection center 502may be implemented as one or more circuits and/or processors.

FIG. 6 is a diagram illustrating a method operational on an informationcollection center for collecting information from various sensingdevices in a cognitive radio network to implement a voting scheme. Thisinformation may be used to determine whether a particular frequency orspectrum band can be used for communications or whether it is occupiedby another user. A network region is mapped or divided into a pluralityof sectors, where each sector is a portion of the region 602. A subsetof sectors is selected from the plurality of sectors 604.

A sensing device from within each sector in the subset of sectors isthen selected 606. Such selection may be done based on intra-sectorspatial randomness. In one example, the same number of devices from eachsector in the subset of sectors is used to collect information. In oneexample, randomly selecting a device from within each sector in thesubset of sectors may include: (a) selecting a location within eachsector in the subset of sectors, (b) identifying a first device that isclosest to that location, and/or (c) requesting the first device to scana particular frequency spectrum band and indicate whether signal energyabove a particular threshold is detected.

Additionally, a time slot within a sensing window period is selected608. Such selection may be done based on temporal randomness. Forinstance, the sensing window period may have a fixed time interval T,but the start of the time slot is randomly selected for each sensingwindow period. This sensing window period may be the period during whichsensing devices attempt to detect whether a signal is present in aparticular spectrum band.

Optionally, the information collection center may send a message to theselected sensing devices to collect frequency spectrum usage informationat the selected time slot for a particular frequency spectrum band 610.

Sensing reports are then received, obtained, and/or collected from theselected sensing devices, where each sensing report is indicative ofwhether the reporting sensing device detected a signal energy above aparticular threshold for the frequency spectrum band 612. For instance,each sensing report may include the frequency spectrum usage informationdetected by a particular sensing device. Each report may be considered avote in a voting scheme that is implemented by the informationcollection center to determine whether a particular frequency spectrumband or channel can be used. Thus, the information collection center maythen determine whether a desired frequency spectrum band or channel canbe used based on the sensing reports 614. For example, the desiredfrequency spectrum band can be used if the number of sensing reportsindicating that no signal energy was detected is greater than thesensing reports indicating that a signal was detected.

Example Sensing Device

FIG. 7 is a block diagram illustrating an example of a sensing devicethat may operate within a cognitive wireless network to collect and/orreport information related to frequency spectrum usage to a networkentity (e.g., information collection center). The sensing device 702 maycomprise a processing circuit 704 coupled to a transceiver 706 thatfacilitates communications over the cognitive wireless network bytransmitting and/or receiving signals via an antenna 708. The sensingdevice 702 may include a frequency spectrum scanning module 710configured to scan a particular frequency spectrum band or channel anddetermine whether a signal energy above a threshold is detected. Thesensing device 702 may also include a global positioning module 714 toascertain its own geographical position within a network region. Suchgeographical position may be an absolute position or a relativeposition. A sensing report generator module 712 may then compileposition information and/or frequency usage information (from thefrequency spectrum scanning module 710) and transmits it an informationcollection center for the wireless network.

FIG. 8 is a diagram illustrating a method operational in a sensingdevice for providing frequency spectrum usage information to acollection center for a cognitive wireless network. A message may bereceived from the cognitive wireless network to collect frequencyspectrum usage information at a defined time slot for a particularfrequency spectrum band 802. Consequently, the sensing device maycollect frequency spectrum usage information at the indicated time slotby scanning the particular spectrum band to determine whether a signalenergy above a particular threshold is detected 804. The sensing devicemay also obtain a geographical position for the sensing device 806. Asensing report may then be generated that includes at least one of thefrequency spectrum usage information and the geographical position 808.The sensing report may then be sent to the collection center 810.Subsequently, the sensing device may receive a transmission from thecognitive wireless network on the frequency spectrum band if thecollection center determines that the frequency spectrum band is unused812.

One or more of the components, steps, and/or functions illustrated inFIGS. 1, 2, 3, 4, 5, 6, 7, and/or 8 may be rearranged and/or combinedinto a single component, step, or function or embodied in severalcomponents, steps, or functions without the features described herein.Additional elements, components, steps, and/or functions may also beadded without departing from the invention. The novel algorithmsdescribed herein may be efficiently implemented in software and/orembedded hardware.

Those of skill in the art would further appreciate that the variousillustrative logical blocks, modules, circuits, and algorithm stepsdescribed in connection with the embodiments disclosed herein may beimplemented as electronic hardware, computer software, or combinationsof both. To clearly illustrate this interchangeability of hardware andsoftware, various illustrative components, blocks, modules, circuits,and steps have been described above generally in terms of theirfunctionality. Whether such functionality is implemented as hardware orsoftware depends upon the particular application and design constraintsimposed on the overall system.

The description of the embodiments is intended to be illustrative, andnot to limit the scope of the claims. As such, the present teachings canbe readily applied to other types of apparatuses and many alternatives,modifications, and variations will be apparent to those skilled in theart.

1. A method for obtaining information from a plurality of sensing devices in a cognitive wireless network, comprising: mapping a region into a plurality of sectors, where each sector is a portion of the region; selecting a subset of sectors from the plurality of sectors; selecting a sensing device from within each sector in the subset of sectors; and collecting sensing reports from each selected sensing device in the subset of sectors, where each sensing report is indicative of whether the sensing device detected a signal energy above a particular threshold in a particular frequency spectrum band.
 2. The method of claim 1, wherein the plurality of sectors are of approximately equal area.
 3. The method of claim 1, wherein each sector in the plurality of sectors are selected to encompass approximately the same number of sensing devices on average.
 4. The method of claim 1, wherein the subset of sectors are randomly selected from the plurality of sectors so as to achieve spatial randomness.
 5. The method of claim 1, wherein the sensing device within each selected sector is randomly selected so as to achieve intra-sector spatial randomness.
 6. The method of claim 1, wherein the sensing reports are collected so as to achieve temporal randomness.
 7. The method of claim 1, further comprising: randomly selecting a time slot within a sensing window period; and sending a message to the selected sensing devices to collect frequency spectrum usage information at the selected time slot for a particular frequency spectrum band, where the collected sensing reports include the frequency spectrum usage information.
 8. The method of claim 7, wherein the sensing window period has a fixed time interval, but the start of the time slot is randomly selected for each sensing window period.
 9. The method of claim 1, further comprising: determining whether a desired frequency spectrum band can be used based on the sensing reports.
 10. The method of claim 9, wherein the desired frequency spectrum band can be used if the number of sensing reports indicating detection of a signal energy above the threshold is less than half of all collected sensing reports.
 11. The method of claim I, wherein the same number of sensing devices from each sector in the subset of sectors are used to collect information.
 12. The method of claim 1, wherein selecting the sensing device from within each sector in the subset of sectors includes selecting a location within a sector in the subset of sectors; identifying a first device that is closest to that location; and requesting the first device to scan the particular spectrum band and indicate whether signal energy above a particular threshold is detected.
 13. An information collection device adapted to collect information from a plurality of sensing devices in a cognitive wireless network, comprising: a region mapping module for mapping a region into a plurality of sectors, where each sector is a portion of the region; a sector selection module for selecting a subset of sectors from the plurality of sectors; a device selector module for selecting a sensing device from within each sector in the subset of sectors from which to collect information; and a sensing report collector for collecting sensing reports from each selected sensing device in the subset of sectors, where each sensing report is indicative of whether the sensing device detected a signal energy above a particular threshold in a particular frequency spectrum band.
 14. The device of claim 13, wherein the subset of sectors are randomly selected from the plurality of sectors so as to achieve spatial randomness and the sensing device within each selected sector is randomly selected so as to achieve intra-sector spatial randomness.
 15. The device of claim 13, wherein the sensing reports are collected so as to achieve temporal randomness.
 16. The device of claim 13, further comprising: a sensing time slot selector for randomly selecting a time slot within a sensing window period; and a sensing time slot selector for sending a message to the selected sensing devices to collect frequency spectrum usage information at the selected time slot for a particular frequency spectrum band, where the collected sensing reports include the frequency spectrum usage information.
 17. The device of claim 16, wherein the sensing window period has a fixed time interval, but the start of the time slot is randomly selected for each sensing window period.
 18. The device of claim 13, further comprising: a channel selector module for determining whether the desired frequency spectrum band can be used by the cognitive wireless network based on the sensing reports.
 19. The device of claim 18, wherein such frequency spectrum band can be used if the number of sensing reports indicating detection of a signal energy above the threshold is less than half of all collected sensing reports.
 20. The device of claim 13, wherein the same number of sensing devices from each sector in the subset of sectors are used to collect information.
 21. The device of claim 13, wherein randomly selecting a device from within each sector in the subset of sectors includes selecting a location within a sector in the subset of sectors; identifying a first device that is closest to that location; and requesting the first device to scan the particular spectrum band and indicate whether signal energy above a particular threshold is detected.
 22. An information collection device adapted to collect information from a plurality of sensing devices in a cognitive wireless network, comprising: means for mapping a region into a plurality of sectors, where each sector is a portion of the region; means for selecting a subset of sectors from the plurality of sectors; means for selecting a sensing device from within each sector in the subset of sectors; and means for collecting sensing reports from each selected sensing device in the subset of sectors, where each sensing report is indicative of whether the sensing device detected a signal energy above a particular threshold in a particular frequency spectrum band.
 23. The device of claim 22, further comprising: means for randomly selecting a time slot within a sensing window period; and means for sending a message to the selected sensing devices to collect frequency spectrum usage information at the selected time slot for a particular frequency spectrum band, where the collected sensing reports include the frequency spectrum usage information.
 24. A processor including a processing circuit adapted to: map a region into a plurality of sectors, where each sector is a portion of the region; select a subset of sectors from the plurality of sectors; select a sensing device from within each sector in the subset of sectors; and collect sensing reports from each selected sensing device in the subset of sectors, where each sensing report is indicative of whether the sensing device detected a signal energy above a particular threshold in a particular frequency spectrum band.
 25. A machine-readable medium comprising instructions for collecting information from a plurality of sensing devices in a cognitive wireless network, which when executed by one or more processors causes the processors to: map a region into a plurality of sectors, where each sector is a portion of the select a subset of sectors from the plurality of sectors; randomly select a sensing device from within each sector in the subset of sectors; collect sensing reports from each selected sensing device in the subset of sectors, where each sensing report is indicative of whether the sensing device detected a signal energy above a particular threshold in a particular frequency spectrum band.
 26. A method operational in a sensing device for providing frequency spectrum usage information to a collection center for a cognitive wireless network, comprising: receiving a message from the cognitive wireless network to collect frequency spectrum usage information at a defined time slot for a particular frequency spectrum band; generating a sensing report that includes the frequency spectrum usage information; and sending the sensing report to the collection center.
 27. The method of claim 26, further comprising: obtaining a geographical position for the sensing device; and including the geographical position as part of the sensing report.
 28. The method of claim 26, further comprising: collecting frequency spectrum usage information at the indicated time slot.
 29. The method of claim 26, wherein collecting the frequency spectrum usage information includes scanning the particular spectrum band to determine whether a signal energy above a particular threshold is detected.
 30. The method of claim 26, further comprising: receiving a transmission from the cognitive wireless network on the frequency spectrum band if the collection center determines that the frequency spectrum band is unused.
 31. A sensing device adapted to provide frequency spectrum usage information to a collection center for a cognitive wireless network, comprising: a transceiver for receiving a message from the cognitive wireless network to collect frequency spectrum usage information at a defined time slot for a particular frequency spectrum band; and a sensing report generator module for generating a sensing report that includes the frequency spectrum usage information, wherein the sensing report is sent to the collection center via the transceiver.
 32. The device of claim 31, further comprising: a global positioning module for obtaining a geographical position for the sensing device, wherein the sensing report generator module is adapted to include the geographical position as part of the sensing report.
 33. The device of claim 31, further comprising: a scanning module adapted to collect the frequency spectrum usage information at the indicated time slot by scanning the particular spectrum band to determine whether a signal energy above a particular threshold is detected.
 34. A sensing device adapted to provide frequency spectrum usage information to a collection center for a cognitive wireless network, comprising: means for receiving a message from the cognitive wireless network to collect frequency spectrum usage information at a defined time slot for a particular frequency spectrum band; and means for generating a sensing report that includes the frequency spectrum usage information; and means for sending the sensing report to the collection center.
 35. The device of claim 34, further comprising: means for collecting the frequency spectrum usage information at the indicated time slot by scanning the particular spectrum band to determine whether a signal energy above a particular threshold is detected.
 36. A processor including a processing circuit adapted to: receive a message from a cognitive wireless network to collect frequency spectrum usage information at a defined time slot for a particular frequency spectrum band; generate a sensing report that includes the frequency spectrum usage information; and send the sensing report to a collection center of the cognitive wireless network.
 37. A machine-readable medium comprising instructions operational in a sensing device for providing frequency spectrum usage information to a collection center for a cognitive wireless network, which when executed by one or more processors causes the processors to: receive a message from a cognitive wireless network to collect frequency spectrum usage information at a defined time slot for a particular frequency spectrum band; generate a sensing report that includes the frequency spectrum usage information; and send the sensing report to a collection center of the cognitive wireless network. 